2.1. Data collection and preprocessing

The steps in the analysis of the entire research are shown in Figure 1 . First, we obtained gene expression datasets of RA synovial samples ({"type":"entrez-geo","attrs":{"text":"GSE77298","term_id":"77298"}}GSE77298, {"type":"entrez-geo","attrs":{"text":"GSE55235","term_id":"55235"}}GSE55235, {"type":"entrez-geo","attrs":{"text":"GSE12021","term_id":"12021"}}GSE12021, and {"type":"entrez-geo","attrs":{"text":"GSE55457","term_id":"55457"}}GSE55457) from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) (12). These datasets included 87 synovial samples (36 normal control samples and 51 RA samples) ( Table 1 ). {"type":"entrez-geo","attrs":{"text":"GSE55457","term_id":"55457"}}GSE55457 was used as an external validation dataset, whereas the other datasets were merged and normalized for data analysis as a training set using the sva package (13). Common genes across each dataset were identified for further analysis.

The flowchart depicting the investigation procedure. GSEA, gene set enrichment analysis; GSVA, gene set variation analysis; WGCNA, weighted gene co-expression network construction analysis; DEGs, differentially expressed genes; ssGSEA, single-sample gene set enrichment analysis; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; LASSO, least absolute shrinkage and selection operator; RF, random forest; SVM-RFE, support vector machine–recursive feature elimination; ROC, receiver operating characteristic curve; TFs, transcription factors; miRNAs, microRNAs; DCA, decision curve analysis.

Information of datasets obtained from GEO.